Traveler tracking system

ABSTRACT

Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/193,335, filed Nov. 16, 2018, entitled “TRAVELER TRACKING SYSTEM,”which is a continuation of U.S. patent application Ser. No. 14/538,580,filed Nov. 11, 2014, entitled “TRAVELER TRACKING SYSTEM,” which claimspriority from U.S. Provisional Patent Application No. 61/902,744,entitled “TRAVELER TRACKING SYSTEM,” including Appendix A and B, filedNov. 11, 2013, and from U.S. Provisional Patent Application No.62/016,327, entitled “TRAVELER TRACKING SYSTEM,” filed Jun. 24, 2014,all of which are incorporated by reference in their entireties.

BACKGROUND

Guests usually carry one or more electronic devices during their hotelstay. Guests may further connect their devices to the internet throughthe hotel's network management portal system. The hotels may requireguests to pay before accessing the internet. The hotel's networkmanagement system can authorize guests to access the internet.

SUMMARY

The systems and methods described herein can be implemented by acomputer system comprising computer hardware. The computer system mayinclude one or more physical computing devices, which may begeographically dispersed or co-located.

Certain aspects, advantages and novel features of the inventions aredescribed herein. It is to be understood that not necessarily all suchadvantages may be achieved in accordance with any particular embodimentof the inventions disclosed herein. Thus, the inventions disclosedherein may be embodied or carried out in a manner that achieves orselects one advantage or group of advantages as taught herein withoutnecessarily achieving other advantages as may be taught or suggestedherein.

In certain embodiments, a system described herein can include a memoryand a computing system in communication with said memory. The computingsystem can be configured to receive data from network managementsystems. In some embodiments, the network management systems areconfigured to receive plurality of connection requests from a pluralityof user devices. The network management systems may provide transmissioncontrol protocol handshake completion data responsive to said connectionrequests. The data received for each user device of the plurality ofuser devices may include one or more of the following: a deviceidentifier included in one or more packets sent from the user devicerequesting access to an external network resource using a networkmanagement system at a venue; one or more attributes associated with thevenue; user data associated with the user device; and connection dataassociated with communications between the user device and said externalnetwork resource.

The system of the preceding paragraph can have any sub-combination ofthe following features: wherein said device identifier comprises a MACaddress; wherein said one or more attributes associated with the venueinclude at least one of geographical location of the venue, classidentifier of the venue, distance of the venue from the airport, numberof conference rooms, average price of rooms at the venue; wherein saidconnection data include a list of network resources requested from theuser device; wherein said user data include at least one of a loyaltyidentifier associated with the venue and an email address associatedwith the user; wherein said computing system can calculate a frequencyof travel of a user based in part on the received data; wherein saidcomputing system can associate a user with the received data; whereinsaid computing system can determine a correlation between a first userdevice and a second user device based in part on the received data;wherein said computing system can determine a relationship between afirst user using a first user device and a second user using a seconduser device based in part on the received data; wherein said computingsystem can generate a travel map based on received data; wherein saidcomputing system can determine a next travel destination of a user basedin part on the received data; wherein said computing system candetermine out of pattern visits; wherein said computing system cancalculate a loyalty metric number for a user; wherein said computingsystem can send content to one of said network management systems forinjection into a user requested content based in part on the receiveddata; wherein said computing system can generate an alert in response todetecting a first user at a first venue from the received data; whereinsaid computing system can detect clusters based in part on said one ormore attributes associated with the venue included in the received data;wherein said computing system can compare one or more attributes ofvenues visited by a user with one or more detected clusters anddetermine a probability score that a user belongs to that cluster basedat least on the said comparison; and wherein said computing system cangenerate content configured to be injected by the network managementsystem based on said probability score.

In certain embodiments, a system described herein can include a memoryand a computing system in communication with said memory. The computingsystem can be configured to receive data from network managementsystems. In some embodiments, the network management systems areconfigured to receive plurality of connection requests from a pluralityof user devices. The network management systems may provide transmissioncontrol protocol handshake completion data responsive to said connectionrequests. The data received for each user device of the plurality ofuser devices may include one or more of the following: a deviceidentifier included in one or more packets sent from the user devicerequesting access to an external network resource using a networkmanagement system at a venue, and geographical location data associatedwith the venue. The computing system can generate a travel map based onthe received data. In some embodiments, the computer system candetermine a first geographical location of a first user devicerequesting access to external network resources at a network managementportal from a first venue at the first geographical location. Further,the computing system can identify a second geographical location that afirst user of the first user device is likely to travel to next from thefirst geographical location.

In certain embodiments, a system described herein can include a memoryand a computing system in communication with said memory. The computingsystem can be configured to receive data from network managementsystems. In some embodiments, the network management systems areconfigured to receive plurality of connection requests from a pluralityof user devices. The network management systems may provide transmissioncontrol protocol handshake completion data responsive to said connectionrequests. The data received for each user device of the plurality ofuser devices may include one or more of the following: a deviceidentifier included in one or more packets sent from the user devicerequesting access to an external network resource using a networkmanagement system at a venue and user data associated with the userdevice. The computing system can store association between user datawith respective device identifiers; in the data store. In someembodiments, the computing system can receive a first device identifierincluded in one or more packets sent from a first user device requestingaccess to an external network resource using one of said networkmanagement systems. Further, the computing system can determine if thefirst device identifier is included in the data store. The computingsystem can also associate the first device with one of the users storedin the data store based in part on the received data.

In some embodiments, a method for managing visitors of a venue caninclude detecting a plurality of requests for connection to networkresources during visits at a plurality of venues from a user deviceassociated with a first user. The method can further include trackingthe visits based in part on a device identifier of the user deviceretrieved from said connection requests. In some embodiments, the methodcan include determining a loyalty metric score for the first user basedin part on said tracking. The method can also include adjusting travelertreatment based in part on the determined loyalty metric score.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments disclosed herein are described below with reference to thedrawings. Throughout the drawings, reference numbers are re-used toindicate correspondence between referenced elements. The drawings areprovided to illustrate embodiments of the inventions described hereinand not to limit the scope thereof

FIG. 1 illustrates an embodiment of a computing environment including aguest monitoring system that can enable hotels to monitor their guests'travel patterns.

FIG. 2 illustrates an embodiment of a process for tracking guestinformation at a network management portal system.

FIGS. 3A-B illustrate an embodiment of a process for determining thenext travel destination of a guest.

FIG. 4 illustrates an embodiment of a process for determining out ofpattern hotel stays for a guest and adjusting guest treatment level bythe hotel.

FIG. 5A illustrates an embodiment of a network management portal devicemanaging multiple guest devices.

FIG. 5B illustrates a mapping of guests and their associated informationbased on their device MAC addresses stored in a database.

FIG. 6 illustrates an embodiment a process for determining anassociation between two hotel guests.

FIG. 7 illustrates an embodiment of a logical data model for storinginformation about users and visits.

FIG. 8 illustrates an embodiment of a guest monitoring system that cansend messages to the user devices based on the analytics processes.

FIG. 9 illustrates an example information connectivity model for a guestmonitoring system.

FIG. 10 shows an example cluster analysis using KXEN® unsupervisedlearning algorithms.

FIG. 11 shows segment probabilities for a group of travelers based on acluster analysis.

FIG. 12 illustrates an embodiment of guest monitoring system receivingdata from a wide variety of sources.

FIG. 13 illustrates an embodiment of the analytics module that includesmultiple sub-modules.

DETAILED DESCRIPTION

-   I. Introduction

Guests typically carry one or more electronic devices during their stayat a hotel. Furthermore, guests may also want to use their electronicdevices to connect to the internet during their stay. Hotels can providea network management portal system to enable guest devices to connect tothe internet. Before authorizing connection, hotels may require gueststo enter credit card or other login information. This information is,however, limited and may not allow hotel operators to analyze thepreferences and travel patterns of their guests. Preferences mayinclude, for example, location of the venue such as whether a hotel islocated in downtown or close to the airport or attributes of the venuesuch whether a hotel has meeting rooms. For instance, a hotel chainoperator may want to know why in a particular city, their guest isstaying at a different hotel instead of one of their hotels. Hoteloperators may also want to know the next likely destination of theirguest. Moreover, hotel operators may want to target frequent travelers(road warriors) and it may be beneficial to know their loyalty to aparticular chain and other preferences.

This disclosure describes embodiments of a guest monitoring system thatcan track guests and provide hotel operators with data analysis toincrease their customer satisfaction and business efficiency. Forexample, the guest monitoring system can identify and alert hospitalityservices (e.g. hotels, coffee shops, restaurants, etc.) potentiallyvaluable customers previously unknown to the service. This recognitioncan be provided in real time to on-site venue managing systems,informing them that a potentially valuable traveler has arrived onpremise. In some embodiments, real time includes less than 10 seconds,or less than 30 seconds, or less than 1 minute. In some embodiments, thereports generated by the guest monitoring system may be delivered weeklyor monthly. The alerts and/or reports can be used by services to offerspecial or directed treatment to the valuable traveler, thereby creatinga positive impression on the traveler concurrent with his stay. Inanother aspect, information can be provided in various batch-orientedmodes to support service individualization, promotional campaigns,upselling, or simply better understanding of travelers.

Accordingly, in some embodiments, the guest monitoring system can tracktravelers by recognizing their use of internet access from hotel roomsand other locations associated with the non-permanent stay. Theseinclude coffee shops, restaurants, stadia etc. Travelers can beclassified by frequency of travel, hotel chain preferences, propertypreferences, cities visited and other visitation patterns. Visit datacan be added to the record, including property identifiers, propertyattributes, room preferences, length of stay, room costs, etc. The guestmonitoring system can capture the visitation data in real time usingnetwork management portal systems. The location of the networkmanagement portal system can provide additional specifics. Location caninclude geographical location of the venue or a relative location withina particular venue. In some embodiments, the location information isstored in a memory of network management portal. For instance, in thecase of a hotel property, additional details may include hotelinformation, room information, rate schedules, features, and roomcharges. Additional characterization of each specific location can alsobe gathered from public domain sources. The GMS can automaticallyretrieve the specific data.

Moreover, confidentiality of the traveler can be preserved as per thestandards described in the Fair Credit Reporting Act. For example,instead of using names, the traveler can be identified with a code. Thecode may be related to the device used for accessing network such as aMAC address or loyalty number. In some instances, each venue system mayprovide an additional identifier, derived from their loyalty program orregistration information. Delivery of alerts and other information to avenue system can be in the form of reports, bulletins or specialanalyses. Further, marketing information derived from the guestmonitoring system can be provided as lists or database files, keyed by atraveler identifier. The venue systems can subsequently personalize theinformation. For products or services which involve the delivery ofreal-time alerts to an on-site venue system indicating the presence of apotentially valuable patron, these may use some non-personal identifier(a hotel room number, for example). Accordingly, confidentiality of thetraveler can be preserved.

In some embodiments, the guest monitoring system can provide teal timeinformation to a specific venue location about a specific visitor via alightweight application, running on a network management portal system.This application can support push or pull notification from the guestmonitoring system. A venue system may trigger certain analytics asneeded. This may be supported by a TCP protocol built into theapplication.

-   II. Example Guest Monitoring System

FIG. 1 illustrates an embodiment of a computing environment 100 forproviding venue operators with access to a guest monitoring system 110that can assist venue operators in tracking their guests and travelpatterns. In an embodiment, a venue operator can be a hotel or a chainof hotels. Venue operators can also include coffee shops, restaurants,events centers or any operators providing their guests access tointernet or an external network through a network management portalsystem 108. Hotel operators can use the guest monitoring system 110 tomonitor their guests, for example, to track travel patterns and todetermine guest loyalty.

In the computing environment 100, the network management portal system108 can facilitate a request from a guest device 130 to access anexternal resource on a network. The external resource can be, forexample, a website. In an embodiment, the network management portalsystem 108 includes a gateway device. In other embodiments, the networkmanagement portal system 108 can include an access point and acontroller. In the process of facilitating the request from the guestdevice 130 to access the external resource, the network managementportal system 108 can determine a unique identifier associated with theguest device 130. The unique identifier can be the MAC address of thedevice 130. In some embodiments, the unique identifier can include emailaddress or other identifying information that may be supported by802.11u and Hotspot 2.0 standards. In further embodiments, the uniqueidentifier can be associated with credentials stored in the SIM card ofthe device 130. The unique identifiers may be associated with thenetwork interface of the device 130. As an example, many laptops havetwo network interfaces, one for wired connections and one for wirelessconnections. Each of these may have a separate MAC address. The networkmanagement portal system 108 can send the unique identifier along withother information associated with the device 130 and its user to theguest monitoring system 110. The network management portal system 108can also capture communications between the device 130 and the externalsite. For example, the network management portal system 108 can capturepackets received from the guest device 130 and store some or all of thepackets in a data repository 118. The network management portal system108 can send this captured communications data to the guest monitoringsystem 110. The network management portal system 108 may include aninternal memory for storing network management portal system attributesand/or captured packet data. The network management portal systemattributes may include location information and/or venue specificparameters. In some embodiments, the portal system attributes may bestored externally from the network management portal system 108 in adata repository.

In some embodiments, the network management portal system 108 canrequire authorization before allowing guest devices 130 to access theexternal resource. Some embodiments of the network management portalsystem 108 implementing the authorization process are described more indetail in U.S. patent application Ser. No. 13/659,851, filed Oct. 24,2012, titled “Systems and Methods for Providing Content and Services ona Network System,” incorporate herein by reference in its entirety. Thenetwork management portal system 108 may retrieve the unique identifierof a guest device 130 from a connection request from the guest device130. For example, the network management portal system 108 may receive arequest for a connection to an external network from a guest device 130.The network management portal system 108 may send transmission controlprotocol handshake data to the guest device 130 in response to receivingthe request for connection. The request may include one or more packets.In some embodiments, network management portal system 108 may retrievean identifier from the packets. In an embodiment, the identifierincludes the MAC address of the guest device 130. In some embodiments,the network management portal system 108 may include one or morehardware processors to determine whether the user is authorized based onidentifier of the guest device 130. The network management portal system108 may redirect the guest device 130 based in part on determiningwhether the guest device 130 is authorized to access an externalresource.

In an embodiment, the collections module 112, included in the guestmonitoring system (GMS) 110, can receive the MAC address or otheridentifier of the device 130 along with user data associated with theuser of the device 130. This user data may include credit cardinformation, room number, or personal user identification information(e.g. email address, phone number, hotel loyalty number, socialnetworking account, login ID, etc.). The guest monitoring system 110 canreceive some or all of the data from the network management portalsystem 108. In some embodiments, the guest monitoring system 110 canalso receive data from third party sites 140 or the venue operatorsystems 142. For example, the venue operator systems 142 can include ahotel PMS system. The collection module 112 can store all theinformation associated with the device 130 and its user including theMAC address in a user data repository 118. Accordingly, the user of thedevice 130 can be tracked by the guest monitoring system 110 based onthe MAC address.

In some embodiments, the GMS 110 can determine patterns of users basedon connection requests. The following example illustrates how the GMS110 can determine travel patterns of guests from hotel visits. A hotelmay include a network management portal system 108 to allow their gueststo access internet or other network resources. When a device 130 sendsout a request for connection, the guest monitoring system 110 candetermine whether it has seen this device 130 before based on the MACaddress of the device 130. For example, the guest monitoring system 110may have stored this device information before based on a prior visit toa same or a different hotel. If there is no record of the MAC addressfor the device 130, the guest monitoring system 110 can store the MACaddress of the device and the corresponding profile information (forexample, manufacturer of the device, type of device, guest information,hotel information etc.). The hotel information can include geographicallocation of the hotel and other property details. If there is a recordof the MAC address in the repository 118, the guest monitoring system(GMS) can update the record with the new connection information.

As an example, a guest 102 having a smartphone and a laptop checks intoHotel A in City 1. A network management portal system 108, at Hotel A,can receive a request for connection to the internet from thesmartphone. The network management portal system 108 can send the MACaddress associated with the smartphone to the guest monitoring system110 along with other information described above. Similarly, the networkmanagement portal system 108 can also send the MAC address of the laptopto the GMS 110 when it receives a connection request from the laptop.The GMS 110 can associate the MAC addresses of the laptop and thesmartphone with the hotel guest 102. The guest 102 then travels to City2 and checks into Hotel B. A network management portal system 108, atHotel B, can receive request for connection to the internet from thelaptop. The network management portal system 108 can subsequently sendthis data to the GMS 110. The GMS 110 can determine whether this MACaddress is in the repository. In this example, the laptop was previouslystored during connection request at Hotel A. The GMS 110 can update therecords and include travel pattern, for example, City 1 to City 2. TheGMS 110 can also store where the guest stayed in each of those cities.Accordingly, the GMS 110, can store guest travel patterns.

The collection module 112 can automatically consolidate multipledevices, each including a MAC address, to a single user. The collectionmodule 112 can use tracked communications (e.g. email address, loyaltynumber, name, or other personal information) in conjunction with otherstored data to consolidate multiple devices. When a guest uses a newdevice with a new identifier to access a network, the collections module112 can maintain continuity based on other MAC addresses associated withthe guest and tracked communications as described herein. Theparticipating location (venue) may also provide additional informationabout the guest and his or her visit, including room number. In somecases, the guest monitoring system 110 can retrieve the guest's loyaltyprogram ID. Accordingly, the guest monitoring system 110 can use some orall of this data to link the new device to past history. In certainembodiments, the linking of devices to a user can advantageously offerpersistence of guest information over an extended period of time.

The analytics module 114 can perform various analytics on the datastored repository 118. For example, based on plurality of guests' travelpatterns identified from tracking MAC addresses, the GMS 110 can createa travel map. The travel map can indicate travel hubs and hoppingprobabilities between different cities. The guest monitoring system 110can identify based on the guest's own travel patterns and the travel mapwhere the guest is most likely to go next. For example, the guestmonitoring system 110 can identify in one embodiment that a gueststaying in a hotel in New York is most likely to travel to Boston next.Based on this information, the content injection module 116 can insertcontent (e.g. advertisements) through the network management portalsystem 108. The content injection module can also insert content basedon device type which can automatically be identified by the GMS (basedon MAC address and device information received by the network managementportal system 108). If the same device (e.g. an iPhone) is seen for acertain period of time, the analytics module 114 can identify that thedevice is an iPhone and may suggest Samsung advertisements to the guest.The GMS 110 can also monitor for how long has the user been using aparticular device and determine if they would likely be looking for anew device based on the detected time of usage.

The analytic module 114 can also determine out of pattern information.For example, the analytics module 114 can determine that a guest staysin Hotel chain A in most cities, but stays in Hotel chain B in somecities. Using this information, Hotel A can target the guest in thecities that the guest is staying in Hotel B.

The analytics module 114 can also identify frequency of hotel stays forthe guests and offer service based on the frequency. For example, somehotels may want to offer special services to road warriors. Frequencymay depend on number of visits to hotels in a given time period. Thetime period may be one year or less. In some embodiments, the timeperiod may be more than a year and may correspond to a lifetime of auser.

The administrator system 144 can provide one or more user interfaces tomanage and access the guest monitoring system 110.

The guest monitoring system 110 can be implemented in computer hardwareand/or software. The guest monitoring system 110 can execute on one ormore computing devices, such as one or more physical server computers.The network management portal system 108 may also implement some or allmodules of the guest monitoring system 110. In implementations where theguest monitoring system 110 is implemented on multiple servers, theseservers can be co-located or can be geographically separate (such as inseparate data centers). In addition, the guest monitoring system 110 canbe implemented in one or more virtual machines that execute on aphysical server or group of servers. Further, the guest monitoringsystem 110 can be hosted in a cloud computing environment, such as inthe Amazon Web Services (AWS) Elastic Compute Cloud (EC2) or theMicrosoft® Windows® Azure Platform. The user devices 130 can bedesktops, laptops, netbooks, tablet computers, smartphones,smartwatches, PDAs (personal digital assistants), servers, e-bookreaders, video game platforms, television set-top boxes (or simply atelevision with computing capability), a kiosk, combinations of thesame, or the like.

FIG. 2 illustrates an embodiment of a network management process 200.The network management process 200 can be implemented by any of thesystems described above. In an embodiment, the network managementprocess 200 can be implemented in a network management portal system108. The network management process 200 can begin at block 202 where anetwork management portal system may receive a request for connection toaccess an external server from a user device. The connection request mayinclude a device identifier of the user device. The device identifiercan be include, for example, in the network packets. In someembodiments, the device identifier can be a MAC address of the userdevice. Accordingly, at block 204, the network management portal system108 can extract MAC device of the user device from the connectionrequest. In some embodiments, the network management system 108 can sendthe device identifier to guest monitoring system 110 at block 206. Asdiscussed herein, the guest monitoring system 110 may be implementedexternal to the network gateways system 108. In some embodiments, someor all of the modules of the guest monitoring system 110 may beimplemented in the network management portal system 108. In someembodiments, the network management system 108 may determine whether theuser device is authorized to access external network resources at block208. The authorization may depend on the MAC address of the user device.Authorization and the transmission control protocol handshake completionis discussed more in detail in application Ser. No. 14/094,712 titled“Systems and Methods For Providing Content and Services on a NetworkSystem,” incorporated herein by reference in its entirety. At block 210,the network management system 108 can track communications between theuser device and network resources once a connection is established. Forexample, the network management system 108 can store all the requestednetwork resources. the network management system 108 can also store someor all of the packets in the communication. In some embodiments, thenetwork management system 108 can send tracked data to the GMS 110 forstoring in data repository 118.

FIGS. 3A-B illustrates an embodiment of a guest tracking process 300that can be implemented by any of the systems described above. Referringto FIG. 3A, at block 302, the guest monitoring system 110 can receive aMAC address or a device identifier associated with a user deviceattempting to connect to external server from a hotel or other venue. Asdiscussed above, the MAC address may be received from a networkmanagement portal 108 at the hotel. At block 304, the GMS 110 canassociate the MAC address of the user device with a hotel guest. Forexample, the GMS 110 can lookup previously stored MAC address in datastore 118 to identify the hotel guest. If the MAC address is not foundin the data store 118, the GMS 110 can determine if the device belongsto a user already in the system or if it belongs to a new user asdiscussed below. For new users, the GMS 110 may include a new entry inthe data store 118.

Furthermore, at block 306, the GMS 110 may store communication databetween the user device and network resources. As discussed above, thenetwork management portal 108 can track communications from userdevices. Communication data can include, for example the URL sites thatthe user is visiting or search data such as restaurants or movies. TheGMS 110 can associate communication data with the user in the data store118. The GMS 110 can also receive parameters or attributes related to avenue at block 308. In some embodiments, the GMS 110 can receiveattributes related to a venue from a network management portal system108 that manages access to network resources for users visiting aparticular venue. The network management portal system 108 may storeattributes such as hotel location, hotel star classification, conferencerooms in the hotel, etc. in its memory. In some embodiments, the networkmanagement portal system 108 may retrieve the attributes from a datarepository external from the network gateway system 108. The networkmanagement portal system 108 can send the venue attribute data to theGMS 110. The GMS 110 can associate the venue attribute data with theuser and a particular visit data. In some embodiments, the GMS 110 canreceive a venue name or other venue identifiers (such as locationcoordinates) from the network management system 108 that may enable theGMS 110 to retrieve venue attribute data.

In some embodiments, the GMS 110 can also receive and store parametersassociated with the user device at block 310. The user device parametersmay include, for example, device identifiers, device type (laptops,smartphones, tablets, smartwatch, etc), device operating system, devicemanufacturer, and device network operator (AT&T, Verizon, etc). In someembodiments, the network management system 108 can extract deviceparameters automatically from the device.

Further, at block 312, the GMS 110 can receive and store user dataassociated with a user requesting connection using a network managementsystem 108. The user data can include, for example, hotel loyalty ID,email address, home address, phone number, user ID, password, creditcard data, etc. In some embodiments, the network management portal 108can extract user data from communications received from the user deviceand send the user data to GMS 110. The user device data may also bereceived from hotel systems such as property management systems. In someembodiments, the network management system 108 can receive room numberfrom the connection request and identify user data from hotel systemsbased on the room number. User data may also be obtained duringauthorization of a user device to access network resources. The GMS 110can associate received user data with a user and a particular hotelvisit in the data store 118. Accordingly, the data store 110 can includedata corresponding to users linked with user devices and correlated withhotel visits including attributes of hotels and communication data. Insome embodiments, the data store 110 can automatically collect all thecommunication data, user data, device parameter data, and hotelattributes.

The GMS 110 can analyze the stored data in the data repository 110 asdescribed herein. In some embodiments, the GMS 110 can use some of thedata in the data repository 110 to generate a travel map as illustratedin FIG. 3B. For example, the GMS 110 can monitor several guests as theymove from one hotel to another in different cities as discussed above.Accordingly, the GMS 110 can identify travel hubs based on visitfrequencies at block 316. In some embodiments, the GMS 110 can use thetravel map to identify a next travel destination of the user based on atleast one of the following: user's previous travel pattern, thegenerated travel map, and communication data. For instance, the GMS 110may determine that a user accessing internet from a hotel in London ismost likely to visit Paris next based on travel map probabilities.Further, the GMS 110 can determine from data store 118 that the user hadvisited Paris after London on a previous trip. In some embodiments, theGMS 110 can access tracked communication data from the networkmanagement portal system 108. For instance, the GMS may identify fromthe tracked communications that the user is searching for restaurants inParis or checking in to a flight. Accordingly, the GMS 110 can use oneor more of these metrics determine a user's next travel destination. Insome embodiments, the GMS 110 can inject content through the networkmanagement portal system 108 based on the determination of the nexttravel city. Content may include, for example, advertisement for a hotelor restaurants or deals related to the next travel destination. Contentinjection via a network portal system 108 is described more in detail inU.S. patent application Ser. No. 13/659,851.

FIG. 4 illustrates an embodiment of a guest analytics process 400 thatcan be implemented by any of the systems described above. Some examplesof travel map and predictive analysis are described in Appendix A and Bof the provisional application, Application No. 61/902,744, theappendices and the provisional application incorporated herein byreference. As discussed above, the GMS 110 can use stored data in datarepository 118 for analysis. In one embodiment, the GMS 110 can identifyhigh frequency travelers and/or out of pattern hotel stays from thestored data. Furthermore, the GMS 110 can also generate alerts orreports to modify traveler treatment based on the analysis. Travelertreatment may include vouchers, free wife, travel points, or any otherservice valued by a traveler. As illustrated in FIG. 4 , the process 400can begin at block 402 with storing travel patterns of users asdiscussed above with respect to FIG. 3A. The GMS 110 can determine hotelloyalty of users in traveled cities based on data stored in repository118. In some embodiments, hotel loyalty may depend on the number ofvisits to a particular city and a number of times the user has stayedwith a particular hotel. For example, if the user has made 10 visits toLondon in the last 3 months and has stayed in Hotel A for 6 of thosevisits, the hotel loyalty for Hotel A may be 60%. Further, at block 406,the GMS 110 may determine that the user is a high frequency travelerdepending on the number of visits in a given time period. Hotel chainsmay be interested in targeting high frequency travelers and luring themto stay at their hotels. Thus, in the example above, the hotel may wantto target the high frequency traveler to increase his or her loyaltyscore from 60%. In some embodiments, the GMS 110 can also determine outof pattern hotel stays at block 408. For example, a particular guest maybe staying at Hilton in most cities, but Marriott in some cities. TheGMS 110 can identify such patterns based on the stored data in the datarepository 118. Further, at block 410, the GMS 110 can generate an alertor a report for a hotel management system to adjust traveler treatmentbased on the identified pattern. In some embodiments, the GMS 110 cansend a text alert to a hotel manager or send an alert to hotel systemwhen the valued user is checking in.

FIG. 5A illustrates a mapping of multiple users sharing devices 130. Theguest monitoring system 110 can automatically determine which users areassociated with the devices 130 based on tracked user data stored indata repository 110. In one embodiment, the guest monitoring system 110can identify a user using a shared device based on the trackedcommunications (for example, email address). The GMS may also use roomnumber or other personal user data to identify whether there is anyrelationship between user devices 130. For example, the GMS 110 candetermine whether a laptop and a smartphone are being used by a sameuser based on stored data in data repository 118. The GMS 110 cancompare, for example, login information, loyalty number, location, roomnumber, or personal data such as email address, etc to determine therelationship.

FIG. 5B illustrates a mapping of devices 514, users 512, the associatedinformation 510 stored in data repository 118.

FIG. 6 illustrates an embodiment of a multiple user association process600 that can be implemented by any of the systems described above. Forexample, the GMS 110 can identify if there is a relationship betweenusers of particular devices. Some examples of relationship include:husband-wife, coworkers, etc. The GMS 110 can automatically identify therelationship based on the MAC addresses and the tracked communication.As discussed above, the GMS 110 can compare data corresponding toindividual user devices and tracked communications to determinerelationship between users. The GMS 110 can also query hotel systems toretrieve guest count in the room or whether guests in separate hotelrooms were paid using same company credit card. The GMS 110 can injectcontent based on the relationship, for example, spa advertisement whenthe wife is visiting with husband or a sports game advertisement whencoworkers are around.

FIG. 7 illustrates an embodiment of a logical data model that can beused by the guest monitoring system 110 to store information about usersand visits in one or more data stores 118. Hotel chains typically own orfranchise properties. Properties can include many property attributes,which can be provided by the property system 142 or stored in networkportal system 108, the chain system, and/or 3^(rd) party vendors ofproperty information. These may include pricing structures, customerratings, locale, neighborhood information and much more. To protect atraveler's confidentiality, the basic traveler entity may be keyed by aunique number assigned by the guest monitoring system 110. In anembodiment, a traveler is defined on at least one visit where aninternet access is made through a property's network management portalsystem 108. As described above, the traveler can access the internetthrough one or more devices, each of which may have a unique identifier(e.g., a MAC Address). A visit can be captured as a result of thetraveler's use of one or more devices. The many-to-many relationshipillustrated in FIG. 7 , such as the Device—DeviceUsed—Visit associationimplies that a visit can be defined on one or more MAC Addresses.

FIG. 8 illustrates an embodiment of a guest monitoring system 108 thatcan send messages to the user devices 130 based on the analyticsprocesses described herein. The messages may include, for example,offers or advertisements. The components of the guest monitoring system110 can follow a relational database system model, such as Teradata,capable of supporting high workloads. Workload can include concurrentquery processing, loading, and transformation. The system 110 canreceive feeds around the clock from participating locations, 3^(rd)party and network management system data feeds. The guest monitoringsystem 110 may need to process millions of records, which can bequeried, updated, and loaded every day.

III. Analytics

FIG. 9 illustrates an example information connectivity model for a guestmonitoring system 110. The data corresponding to this model can bestored in data repository 118. The guest monitoring system 110 canimplement variety of data analytics models using the analytics module114. The models can include, for example, social network analysis (SNA),neural network algorithms, machine learning algorithms, clustering,regression, or data exploration. In an embodiment, the data relating tothe cities and venue can be obtained through third party providers andpublic domain records.

In some embodiments, the guest monitoring system 110 can calculatederived data using the collected data. Derived data can enable venues toidentify one or more relationships between the user and venues. Deriveddata can also include numbers, charts and/or graphs. An example ofderived number data is a loyalty metric number. The loyalty metricnumber corresponds to a comparison of visits by a traveler to a firstsubset of venues over a total number of visits by that traveler to asecond subset of venues. The visits metric may depend on the type ofvenue. For example, for a hotel venue, visit metric may relate tonumbers of nights stayed at a hotel room or length of stay. In someembodiments, each week of stay may constitute one visit. A set ofconsecutive day stay may also count as one visit. As an additionalexample, in the case for a coffee shop, restaurant, or other serviceprovider venue, each time a payment is made or a service is used mayconstitute one visit.

In one embodiment, the loyalty metric number is a ratio of visits by atraveler to a property as a percent of total visits by that traveler toall properties. As an example, a traveler may have stayed in hotels forten weeks in the last year. Using the metric of one week as one visit,then that's ten visits in the last year. If out of those ten visits, thetraveler stays at Hotel A for 3 weeks (3 visits), Hotel B for 3 weeks (3visits), and Hotel C for 4 weeks (4 visits); then the loyalty metricnumbers for Hotel A, B, and C is 30%, 30%, and 40%, respectively. Theloyalty metric number may also be calculated over other groupingcriteria including on-site behavior such as amount of money spent at afirst subset of venues as compared to a second subset of venues.

The subsets of venues may be defined over a number of grouping criteria.In an embodiment, venues may be grouped into loyalty determinationsubsets. Loyalty determination sets may be defined over any number ofgrouping criteria. For example, the guest monitoring system 110 maygroup venues with respect to hotel chains, or with respect to hotelbrands, or with respect to analytically defined clusters of properties.In an embodiment, loyalty of a specific traveler t to a specific chain(or property) c, is defined as below:

${LM}_{t,c} = \frac{\sum v_{t,c}}{\sum v_{t}}$

where v_(t,c) is the number of visits to property c by a traveler t andΣvt is the total number of visits by the same traveler to all propertiesin the loyalty determination set. Average booking days or averagerevenue may also be taken into account to calculate the loyalty metricnumber. In some embodiments, the metric averages are computed for allvisits so that estimates of a traveler's value metric to the targetproperty, brand, chain, cluster, etc. can be compared to the averagevalue over all properties in the relevant loyalty determination set. Theguest monitoring system 110 can provide the loyalty metric number tovenue systems for a particular guest. Additionally, the guest monitoringsystem 110 can also push content based on the determined loyalty metricnumber.

Hotels (or other guest services) can use the loyalty metric to influencetraveler choices in ways that loyalty programs alone may be unable to dobecause individual hotel chains might not possess data from otherchains. In an embodiment, the guest monitoring system 110 can alerthotels to the presence of high value guests (e.g. road warriors) whostay with them infrequently. For example, the guest monitoring system110 can identify all the high frequency travelers based on detectingtheir mobile computer's connection requests during their travel. When aparticular high frequency traveler visits a hotel (e.g. Hotel A) asdetermined by a connection request, the guest monitoring system 110 cancalculate the loyalty metric number for that traveler with respect toHotel A. The connection request may be a request to connect to thenetwork. In an embodiment, the connection request is automaticallyinitiated between a mobile device of the traveler and a networkmanagement system as soon as the traveler is within the coverage area ofthe network management system. Accordingly, Hotel A may be able toidentify a traveler as soon as he or she enters the premises and triggerone or more response systems. For instance, the guest monitoring system110 can send an alert to a mobile device of the guest services managerat the hotel. In other embodiments, the system 110 can automaticallyplace a request to the room services system to deliver a complimentarybottle of wine along with a voucher for use in the hotel restaurant.Thus, guest monitoring system 110 can enable hotels to target a segmentof customers.

The guest monitoring system 110 can also generate reports of loyaltymetrics for a particular chain to determine attrition and identify atrisk customers. In an embodiment, the guest monitoring system 110 candetermine a rate of change in the loyalty metric over time (e.g. 6months) to identify possible defectors. The guest monitoring system 110can also calculate future or predicted loyalty scores for a new travelerin the system based on determined patterns of the other travelers.Further, as described above, the guest monitoring system 110 canidentify travel patterns including future destinations of visitors.

Segmentation

Clustering or segmenting travelers based on one or more storedattributes in the data store 118 may be useful in identifying certainpatterns or behaviors of the travelers. The analytics module 114 of theguest monitoring system 110 can identify groups or clusters of travelersbased on the stored data by using cluster analysis or other appropriateanalytical methods. For example, the analytics module 114 can identifythat a certain group of travelers prefer hotels with 3 or moreconference rooms. If there are enough travelers in that group, then theanalytics module 114 can create a cluster with 3 or more conferencerooms. Accordingly, clusters can be based on property attributepreference. For example, the analytics module 114 can determine thatthere is a cluster of travelers based on one or more of the followingattributes of the property: hotel age, room size, cost, location (e.g.suburban, rural, airport, downtown), brand, ballroom, conference rooms,etc. Each mathematical cluster can relate to a traveler segment. Forexample, business travelers may prefer hotels with at least a certainnumber of rooms. In certain embodiments, all the data is automaticallycollected by the guest monitoring system 110 through user devices asdescribed above. For example, consider User Alice visiting HotelsH_(A1), H_(A2) . . . H_(Am) and user Bob visiting Hotels H_(B1), H_(B2),. . . H_(Bn). The GMS 110 can store this visit data in data repository118 as discussed above. Further, the GMS 110 may also store a set ofattributes for each of the hotels visited by Bob and Alice. The GMS 110can store the visit data for hundreds of thousands of users and theircorresponding visits to hotels. The GMS 110 can use the stored visitdata to determine clusters and user preferences as described below.

Cluster analysis can find distinct groups based on mathematicalclustering in an n-dimensional hyperspace. Taking cluster analysis as anexample, a traveler may be assigned to a unique cluster or segment basedon comparison of the traveler's attributes and the attributes of thecluster. It is possible that a traveler will not fit well into anysegment. In this case the traveler may be treated as unclassifiable. Insome embodiments, the analytics module 114 can generate 6 or lessclusters. In other embodiments, the analytics module can generate 10 orless clusters. In yet other embodiments, the analytics module cangenerate more than 10 clusters.

An example cluster calculation is described below. Consider in oneembodiment, data related to hotel attributes is stored in a datarepository. In some embodiments, this data is stored while trackingtravelers through identifiers on their guest devices 130 describedherein. In an embodiment, the data can be arranged in a tabular format.The rows are individual hotels. The columns are attributes of thehotels. One attribute (e.g. Column B) might be the number of rooms inthe hotel. Another (e.g. Column C) might be the number of restaurants.Column D might be how far it is from the nearest major airport. Column Emight be the average daily rate. There could be hundreds of additionalattributes related to a hotel, destination, or traveler. Assume 10,000hotels in the spreadsheet (10,001 rows—the first row being the columnheaders). The guest monitoring system 110 can generate clusters based onthe data. In an embodiment, the guest monitoring system 110 can generate5 clusters. The clustering algorithm implemented in the guest monitoringsystem 100 can use the data to determine the best way to fit each row(hotel) into a cluster such that it has more in common with the otherhotels in that cluster than it does with the hotels in the other 4clusters. In making this determination, some of the attributes(properties) of the hotels may be more important than others.

FIG. 10 shows an example cluster analysis using KXEN® unsupervisedlearning algorithms. Five distinct clusters were found, plotted againstthe two factors. The factors can be, for example, star classificationand average daily rate.

In addition to identifying the clusters and assigning travelers to theidentified clusters, the analytics module 114 can determine aprobability that a particular traveler belongs in a particular segment.Accordingly, segment membership can be a probability function thatdefines the likelihood that a Traveler does in fact belong in aparticular segment. Travelers can be assigned a membership probabilityscore which may be expressed as a decimal fraction like “0.71”. In thisexample, the score can indicate a 71% probability (confidencelevel/likelihood) that the traveler is a member of a particular segment.This score may be determined by an analytical model using a combinationof segment number and all useful metrics in the data repository 118. Thesegment probabilities for travelers can also be stored in the datarepository 118 after it is calculated by the analytics module 114 andupdated in time. For a set of travelers, assuming 5 segments, the set ofsegment membership probabilities can be represented as follows:

P _(s,t) =f _(s)(Attributes_(t))

where,

P_(s,t) is the membership probability of Traveler t in Segment s

Attributes_(t) includes the attributes of Traveler t contained in thedata repository 118.

P_(s,t) can be scored by a segment probability model (neural network,logistic regression, decision tree, etc.) trained on all traveler datasupervised by Segment. If there are five segments, each Traveler can bescored by each segment model. Segment membership scores can be added tothe traveler data store 118. FIG. 11 shows segment probabilities for agroup of travelers. For example, the probability of Amy in that segmentis 0.51 or 51%.

The probability distributions can be used to generate marketing models.A marketing model, e.g., for visitation probability to a specific chainor site, may be trained on Visit data, profile data, and segmentprobability scores. The marketing model can be implemented by theanalytics module 114. Clustering or segmentation can enable guestmonitoring system 110 to correlate attributes of hotels with guests andfocus on targeted marketing.

FIG. 12 illustrates that the GMS may collect data from multiple in houseor third party sources.

IV. TERMINOLOGY

A number of computing systems have been described throughout thisdisclosure. The descriptions of these systems are not intended to limitthe teachings or applicability of this disclosure. For example, the usersystems and described herein can generally include any computingdevice(s), such as desktops, laptops, video game platforms, televisionset-top boxes, televisions (e.g., internet TVs), computerizedappliances, and wireless mobile devices (e.g. smart phones, PDAs,tablets, or the like), to name a few. Further, it is possible for theuser systems described herein to be different types of devices, toinclude different applications, or to otherwise be configureddifferently. In addition, the user systems described herein can includeany type of operating system (“OS”). For example, the mobile computingsystems described herein can implement an AndroidTM OS, a Windows® OS, aMac® OS, a Linux or Unix-based OS, or the like.

Further, the processing of the various components of the illustratedsystems can be distributed across multiple machines, networks, and othercomputing resources. In addition, two or more components of a system canbe combined into fewer components. For example, the various systemsillustrated can be distributed across multiple computing systems, orcombined into a single computing system. Further, various components ofthe illustrated systems can be implemented in one or more virtualmachines, rather than in dedicated computer hardware systems. Likewise,the data repositories shown can represent physical and/or logical datastorage, including, for example, storage area networks or otherdistributed storage systems. Moreover, in some embodiments theconnections between the components shown represent possible paths ofdata flow, rather than actual connections between hardware. While someexamples of possible connections are shown, any of the subset of thecomponents shown can communicate with any other subset of components invarious implementations.

Depending on the embodiment, certain acts, events, or functions of anyof the algorithms, methods, or processes described herein can beperformed in a different sequence, can be added, merged, or left outaltogether (e.g., not all described acts or events are necessary for thepractice of the algorithms). Moreover, in certain embodiments, acts orevents can be performed concurrently, e.g., through multi-threadedprocessing, interrupt processing, or multiple processors or processorcores or on other parallel architectures, rather than sequentially.

Each of the various illustrated systems may be implemented as acomputing system that is programmed or configured to perform the variousfunctions described herein. The computing system may include multipledistinct computers or computing devices (e.g., physical servers,workstations, storage arrays, etc.) that communicate and interoperateover a network to perform the described functions. Each such computingdevice typically includes a processor (or multiple processors) thatexecutes program instructions or modules stored in a memory or othernon-transitory computer-readable storage medium. The various functionsdisclosed herein may be embodied in such program instructions, althoughsome or all of the disclosed functions may alternatively be implementedin application-specific circuitry (e.g., ASICs or FPGAs) of the computersystem. Where the computing system includes multiple computing devices,these devices may, but need not, be co-located. The results of thedisclosed methods and tasks may be persistently stored by transformingphysical storage devices, such as solid state memory chips and/ormagnetic disks, into a different state. Each process described may beimplemented by one or more computing devices, such as one or morephysical servers programmed with associated server code.

Conditional language used herein, such as, among others, “can,” “might,”“may,” “e.g.,” and the like, unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or states. Thus, suchconditional language is not generally intended to imply that features,elements and/or states are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or states are included or are to be performed inany particular embodiment. The terms “comprising,” “including,”“having,” and the like are synonymous and are used inclusively, in anopen-ended fashion, and do not exclude additional elements, features,acts, operations, and so forth. Also, the term “or” is used in itsinclusive sense (and not in its exclusive sense) so that when used, forexample, to connect a list of elements, the term “or” means one, some,or all of the elements in the list. In addition, the articles “a” and“an” are to be construed to mean “one or more” or “at least one” unlessspecified otherwise.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it will beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. Thus, nothing inthe foregoing description is intended to imply that any particularfeature, characteristic, step, module, or block is necessary orindispensable. As will be recognized, the processes described herein canbe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features can be used or practicedseparately from others. The scope of protection is defined by theappended claims rather than by the foregoing description.

1.-26. (canceled)
 27. A method, comprising: detecting, by a guestmonitoring system comprising computer hardware, a plurality of requeststo connect to network resources during visits at a plurality of venuesfrom a user device associated with a first user; tracking, by the guestmonitoring system, the visits based in part on an identifier associatedwith the plurality of requests, wherein the tracking comprisesdetermining respective venue attributes corresponding to the pluralityof venues visited by the first user, and storing the venue attributes inconjunction with the identifier; conducting the detection and thetracking for a plurality of users visiting the plurality of venues;determining, by the guest monitoring system, a plurality of clusters ofusers based on the tracked data; determining, by the guest monitoringsystem, a first user's score for a first venue of the plurality ofvenues for the first user based in part on said tracking, wherein thescore indicates a level of the first user's visits to the first venue ofthe plurality of venues; determining, by the guest monitoring system, afirst likelihood of the first user belonging in a first cluster of theplurality of clusters of users; and adjusting, by the guest monitoringsystem, traveler treatment of the first user based in part on the firstuser's score and the first likelihood of the first user belonging in thefirst cluster.
 28. The method of claim 27, further comprising, by theguest monitoring system, determining a travel frequency for the firstuser based in part on said tracking.
 29. The method of claim 27, furthercomprising, by the guest monitoring system, determining out-of-patternvenue visits based in part on said tracking.
 30. The method of claim 27,wherein the identifier comprises a MAC address of the user device. 31.The method of claim 27, wherein the identifier comprises a useridentifier associated with the first user.
 32. The method of claim 27,wherein the plurality of requests are received by a network managementsystem in network communication with the guest monitoring system. 33.The method of claim 27, wherein the plurality of requests comprisepackets including the identifier.
 34. The method of claim 27, furthercomprising: generating, by the guest monitoring system, a travel mapbased on the tracking of the visits; and determining, by the guestmonitoring system, a first geographical location of the user devicerequesting access to external network resources at a network managementportal from a first venue at the first geographical location; andidentifying, by the guest monitoring system, a second geographicallocation that the first user of the user device is likely to travel tonext from the first geographical location.
 35. The method of claim 27,further comprising injecting, by the guest monitoring system, contentinto a user requested content based in part on the tracking.
 36. Themethod of claim 27, further comprising generating an alert in responseto detecting the first user at the first venue from the tracking. 37.The method of claim 27, further comprising determining a secondlikelihood of the first user belonging in a second cluster of theplurality of clusters of users that is different from the first cluster,wherein the second likelihood is lower than the first likelihood.
 38. Asystem, comprising: one or more memory devices memory storingcomputer-executable instructions; and one or more hardware processorsconfigured to execute the computer-executable instructions that, whenexecuted by the one or more hardware processors, configure the one ormore hardware processors to: detect a plurality of connection requestsfrom a user device associated with a first user; track the visits basedin part on an identifier associated with the plurality of connectionrequests; conduct the detection and the tracking for a plurality ofusers visiting the plurality of venues; determine a plurality ofclusters of users based on the tracked data; determine a first user'sscore for a first venue of the plurality of venues for the first userbased in part on said tracking, wherein the score indicates a level ofthe first user's visits to the first venue of the plurality of venues;determine a first likelihood of the first user belonging in a firstcluster of the plurality of clusters of users; and adjust travelertreatment of the first user based in part on the first user's score andthe first likelihood of the first user belonging in the first cluster.39. The system of claim 38, wherein the computer-executable instructionsfurther configure, when executed, the one or more hardware processors todetermine a travel frequency for the first user based in part on saidtracking.
 40. The system of claim 38, wherein the computer-executableinstructions further configure, when executed, the one or more hardwareprocessors to determine venue attributes corresponding to the pluralityof venues, wherein the venue attributes comprise one or more of cost,location, age, and size.
 41. The system of claim 38, wherein theplurality of connection requests are received by a network managementsystem in network communication with the one or more processors.
 42. Thesystem of claim 38, wherein the plurality of connection requests includeinformation usable to determine the identifier.
 43. The system of claim38, wherein the computer-executable instructions further configure, whenexecuted, the one or more hardware processors to: generate a travel mapbased on the tracking of the visits; and determine a first geographicallocation of the user device requesting access to external networkresources at a network management portal from a first venue at the firstgeographical location; and identify a second geographical location thatthe first user of the user device is likely to travel to next from thefirst geographical location.
 44. The system of claim 38, wherein thecomputer-executable instructions further configure, when executed, theone or more hardware processors to inject content into a user requestedcontent based in part on the tracking.
 45. The system of claim 38,wherein the computer-executable instructions further configure, whenexecuted, the one or more hardware processors to generate an alert inresponse to detecting the first user at the first venue from thetracking.
 46. The system of claim 38, wherein the computer-executableinstructions further configure, when executed, the one or more hardwareprocessors to determine a second likelihood of the first user belongingin a second cluster of the plurality of clusters of users that isdifferent from the first cluster, wherein the second likelihood is lowerthan the first likelihood.